Digital services play a crucial role in enhancing manufacturing competitiveness by enabling differentiation, flexibility, customisation, and improved performance through advanced technologies such as the Industrial Internet of Things and Cyber-Physical Systems. While data-driven production logistics (PL) increasingly adopts these technologies to optimise operations, challenges persist in effectively leveraging digital services to enhance performance. This study proposes a value-oriented digital service framework for data-driven PL, integrating principles of value proposition development and value creation. Complemented by a system architecture, the framework identifies the defining characteristics of value-oriented digital services and provides a blueprint for effective performance monitoring. Empirical results from a case study on material handling in commercial vehicle manufacturing demonstrate significant improvements in PL performance metrics, including enhanced on-time delivery and energy efficiency. This study offers practical guidance for managers seeking to design and implement digital services that enhance operational monitoring and decision-making in data-driven PL.

This paper explores the use of digital technology stages and knowledge demand types for achieving energy efficiency. Digital technology stages are the steps toward developing an intelligent and networked factory: computerization, connectivity, visibility, transparency, predictive capacity, and adaptability. Knowledge demand types refer to the knowledge and skills needed to implement energy management through technical, process, and leadership knowledge. Empirical data were collected from a critical single case study at an industrial manufacturing company. The study made two significant contributions. Firstly, it identifies fourteen challenges and improvement potentials when working with energy monitoring, evaluation, and optimization, demonstrating the critical role of digital technology stages and knowledge demand types. Secondly, the study presents a conceptual framework indicating how companies could overcome pitfalls and enhance energy efficiency by combining digital technologies and knowledge demands. Future work will include technical implementations and its connection to knowledge management.

Digital technologies are critical in advancing a human-centered approach to warehouses that account for productivity and staff well-being. These technologies generate data addressing the negative conditions affecting the well-being of staff during order picking (OP), a labour intensive activity. This study analyzes artificial intelligence (AI)-enabled vision systems to enhance human-centricity and improve the generation and analysis of information about tasks executed by staff in OP. The study presents results from a pilot study in automotive manufacturing applying a design science research approach. The results show that AI-enabled vision systems enhance task identification, analysis, and efficiency in OP. The study suggests five actions including staff information, data acquisition, access restriction, data storage, and protection addressing the privacy concerns of these systems. The study discusses how these systems can integrate staff well-being by identifying human factors and outcomes. It offers three contributions: (1) an overview of activities for collecting task information through AI-enabled vision systems in human-centered OP; (2) evidence that existing architectures for human-centered manufacturing are essential for managing privacy implications; and (3) a discussion of the systems’ impact on human factors and performance, and guidelines for developing and implementing these systems in future studies and operational environments.

Dynamic order picking has usually demonstrated significant impacts on production efficiency in warehouse management. In the context of an automotive-part warehouse, this paper addresses a dynamic multi-tour order-picking problem based on a novel attention-aware deep reinforcement learning-based (ADRL) method. The multi-tour represents that one order-picking task must be split into multiple tours due to the cart capacity and the operator’s workload constraints. First, the multi-tour order-picking problem is formulated as a mathematical model, and then reformulated as a Markov decision process. Second, a novel DRL-based method is proposed to solve it effectively. Compared to the existing DRL-based methods, this approach employs multi-head attention to perceive warehouse situations. Additionally, three improvements are proposed to further strengthen the solution quality and generalization, including (1) the extra location representation to align the batch length during training, (2) the dynamic decoding to integrate real-time information of the warehouse environment during inference, and (3) the proximal policy optimization with entropy bonus to facilitate action exploration during training. Finally, comparison experiments based on thousands of order-picking instances from the Swedish warehouse validated that the proposed ADRL could outperform the other twelve DRL-based methods at most by 40.6%, considering the optimization objective. Furthermore, the performance gap between ADRL and seven evolutionary algorithms is controlled within 3%, while ADRL can be hundreds or thousands of times faster than these EAs regarding the solving speed.

This study investigates standardization of data acquisition, analysis and integration according to ISO 23247 for real-time visualization and interoperability in manual order picking processes within the automotive industry. This study addresses fragmented and inconsistent production logistics (PL) data and enhances real-time performance visualization by establishing a structured PL information model based on the ISO 23247 framework. The proposed approach integrates manufacturing data such as order, product, and operator data with PL specific elements including transportation, time, and material. This integrated information model ensures consistent, scalable, and reliable information data flow in PL systems. The practical applicability and validity of the proposed information model are demonstrated through a case study on manual order picking, highlighting its ability to integrate PL activities and support synchronized data exchange within dynamic production environments. The findings contribute to the broader goal of digital transformation in production and logistics by addressing key challenges in data consistency, system integration, and real-time monitoring. Furthermore, the standardized information model developed in this study can be extended to other industrial sectors, such as warehouse automation, to improve production and logistics integration through a unified information model.

Recent publications underscore the critical implications of adapting to dynamic environments forenhancing the performance of material and information flows. This study presents a systematicreview of literature that explores the technological capabilities of smart production logistics (SPL)when applying machine learning (ML) to enhance logistics capabilities in dynamic environments.This study applies inductive theory building and extends existing knowledge about SPL in threeways. First, it describes the role of ML in advancing the logistics capabilities of SPL across variousdimensions, such as time, quality, sustainability, and cost. Second, this study demonstrates the applicationof the component technologies of ML (i.e. scanning, storing, interpreting, executing, andlearning) to attain superior performance in SPL. Third, it outlines how manufacturing companiescan cultivate the technological capabilities of SPL to effectively apply ML. In particular, the studyintroduces a comprehensive framework that establishes the technological foundations of SPL, thusfacilitating the successful integration of ML, and the improvement of logistics capabilities. Finally,the study outlines practical implications for managers and staff responsible for the planning andexecution of tasks, including the movement of materials and information in factories.

This study applies an intersectional approach to address concerns aboutdiversity of data acquisition when applying computer vision systems in humanorder picking. The study draws empirical data from a single case study conductedat an automotive manufacturer. It identifies critical factors of intersectionality forthe use of vision systems to enrich data collection in human order picking at fourlevels including form and function, experience and services, systems and infrastructure,and paradigm and purpose. These findings are helpful for mitigating biasand ensuring accurate representation of the target population in training datasets.The results of our study are indispensable for enhancing human-centricity whenapplying computer vision systems, and facilitating the acquisition of unstructureddata in human order picking. The study contributes to enhancing diversity in humanorder picking, a situation that is highly relevant because of the variations in age,gender, cultural background, and language of staff. The study discusses theoreticalandmanagerial implications of findings, alongside suggestions for future research.

This paper introduces a framework that integrates smart production logistics (SPL) with network diagrams. This integration enhances visibility in the material and information flow within the manufacturing sector, thereby adding value through data visualization. Drawing from a detailed case study in the automotive industry, we outline the essential components of network diagrams that are tailored to depict spatial-temporal data linked with material handling processes in an SPL context. This integrated approach presents managers with a new tool for optimizing planning and executing tasks related to the transport of materials and information. Furthermore, while the framework brings about significant technological progress, it also emphasizes the managerial implications of SPL data visualization. In particular, it highlights its potential to foster informed decision-making, resource optimization, and strategic forecasting. The paper also discusses prospective research avenues, stressing the importance of dynamic diagrams that decode complex patterns from digital data and the incorporation of sustainability metrics in SPL assessments.

This paper introduces a framework that integrates smart production logistics(SPL) with network diagrams. This integration enhances visibility in the materialand information flow within the manufacturing sector, thereby adding valuethrough data visualization. Drawing from a detailed case study in the automotiveindustry, we outline the essential components of network diagrams that are tailoredto depict spatial-temporal data linked with material handling processes in an SPLcontext. This integrated approach presents managers with a new tool for optimizingplanning and executing tasks related to the transport of materials and information.Furthermore, while the framework brings about significant technological progress,it also emphasizes the managerial implications of SPL data visualization. In particular,it highlights its potential to foster informed decision-making, resource optimization,and strategic forecasting. The paper also discusses prospective researchavenues, stressing the importance of dynamic diagrams that decode complex patternsfrom digital data and the incorporation of sustainability metrics in SPL assessments.

Order picking in manufacturing warehouses is a labor intensive activity with critical implications to the well-being of staff and operational performance of companies. This study addresses the need for applying digital technologies that lead to enhancing a human-centered approach in order picking. It proposes the use of artificial intelligence (AI)-enabled vision systems to facilitate the generation and analysis of information about tasks in manufacturing warehouses. We present the results of a collaborative project between academic and industrial partners from a case in automotive manufacturing. This consists of the development of a pilot study in a laboratory environment and includes two findings. First, we show the steps of implementing an AI-enabled vision system in order picking. This findings is important for automatically generating and analyzing information of tasks in order picking such as setup, travel, search, and picking of parts, which directly affect staff performance. Second, we discuss the implications of this findings for manufacturing companies and its contribution a future in order picking with improved human-centricity.